A novel framework for accurately quantifying wetland depression water storage capacity with coarse-resolution terrain data

Abstract. Accurate quantification of wetland depression water storage capacity (WDWSC) is imperative for comprehending the wetland hydrological regulation functions to support integrated water resources management. Considering the challenges posed by the high acquisition cost of high-resolution LiDAR DEM or the absence of field measurements for most wetland areas, urgent attention is required to develop an accurate estimation framework for WDWSC using open-source, low-cost, multi-source remote sensing data. In response, we developed a novel framework, WetlandSCB, utilizing coarse-resolution terrain data for accurate estimation of WDWSC. This framework overcame several technical difficulties, including biases in above-water topography, incompleteness and inaccuracy of wetland depression identification, and the absence of bathymetry. Validation and application of the framework were conducted in two national nature reserves of northeast China. The study demonstrated that integrating priority-flood algorithm, morphological operators and prior information can accurately delineate the wetland depression distribution with overall accuracy and Kappa coefficient both exceeding 0.95. The use of water occurrence map can effectively correct numerical biases in above-water topography with Pearson coefficient and R² increasing by 0.33 and 0.38 respectively. Coupling spatial prediction and modeling with remote sensing techniques yielded highly accurate bathymetry estimates, with <3 % relative error compared to filed measurements. Overall, the WetlandSCB framework achieved estimation of WDWSC with <10 % relative error compared to field topographic and bathymetric measurements. The framework and its concept are transferable to other wetland areas globally where field measurements and/or high-resolution terrain data are unavailable, contributing to a major technical advancement in estimating WDWSC in river basins.